Welcome to the first issue of Conversations@Tangible for 2025! Our theme for the year will focus on Artificial Intelligence and its transformative impact on branding. To start, our 51st issue will explore how AI has forced a paradigm shift in the way we conduct market research and formulate brand strategies.
Leveraging AI for Better Market Research and Brand Strategies
Dubbed the 4th industrial revolution, Artificial Intelligence (AI) is here to stay. But the speed and intensity with which AI has invaded our lives in recent years has been a source of terror and excitement for people around the world. Uncertainty abounds as employers leap to explore the benefits of widespread automation, while employees anxiously stress about job security. So, the question remains: how do we take advantage of AI tools effectively, efficiently, and ethically? Below, we highlight the transformative ways AI has impacted market research and brand strategy, its dangers and shortcomings, and its role in the future of branding.
5 Ways AI is Impacting Market Research
AI is more than just the next step in market research – it is the dawn of a radical frontier. Its potential to increase productivity, amplify our ability to predict consumer behaviour, and enhance targeted marketing capabilities is immeasurable.
1. Automation of Data Collection and Management
With the support of AI, companies can automate data collection and management to streamline the research process. Gone are the days of manually gathering and cleaning endless oodles of data. Adaptable conversational chatbots like ChatGPT can help design and administer surveys tailored to targeted segments of the population. Parameters can be set to automatically catalogue the responses by demographic, psychographic, or behavioural qualifications, making it quick and cost effective to extract meaningful insights down the line.
AI algorithms can also monitor data quality by cleaning up irrelevant, incomplete, or duplicated responses. Companies like Synthesized and Private AI are trained to remove Personally Identifiable Information (PII) from survey results, effectively preserving data integrity, usability, and ensuring the information complies with privacy regulations.
2. Optimisation of Data Analysis, Visualisation and Reporting
Analysing consumer data from the internet has morphed from a mere Herculean task to an epic Sisyphean nightmare. An estimated 149 zettabytes of data were created in 2024. That’s 400 million terabytes of data generated every, single day!
Luckily, generative AI software is well-equipped to extract key insights from mountains of information. Researchers no longer need to dredge through the digital slog of social media posts, product reviews, and public forums. Natural Language Processing AIs (NLPs) analyse qualitative data online to uncover important market trends and sentiments towards a brand. They can also find patterns in survey responses and focus group interviews to increase our understanding of target markets and consumer behaviour.
3. The Rise of Predictive Analytics
Accurate and efficient predictive modeling is the holy grail of corporate strategy. Imagine the ability to anticipate what consumers want, when they want it, and producing the exact amount needed to maximise profit, while minimising costs.
Today, AI can analyse industry patterns and real-time information to create models that forecast market trends. Machine learning algorithms like Pecan use historical data on consumer behaviour, preference, and spending habits to stay ahead of demand. These predictive models can determine which customers are most likely to be swayed by targeted marketing, to be repeat customers, or to switch to a competing product. As a result, brands will know which sectors of the population they should focus on.
4. The Wonders of Synthetic Data
AI’s generate synthetic data by mimicking existing consumer profiles. The potential here is limitless, offering valuable opportunities to enhance market research and brand strategy. Researchers use the artificial data as a supplement to real data when they conduct market studies or form predictive models because it increases sample size and diversity, leading to stronger findings. It can also extrapolate results from hard-to-reach segments of the population to draw more robust conclusions.
Practically speaking, firms can use this technology to test the performance of new brand campaigns on artificial markets without fear of leaking intellectual property. Finally, synthetic data is far more efficient than collecting and managing real data, requiring less time and fewer resources because market profiles can be generated automatically.
Read more about synthetic data from this Qualtrics article
5. Personalisation and Market Segmentation
Market research will become highly targeted in the years to come. Traditionally, market segmentation relied on surface level demographic qualities like age, gender, income and location. Now, browsing histories, political views, social media posts, and past purchases can be used to create tailored marketing strategies and product recommendations fit to each person’s behaviour profile.
Such a degree of personalisation allows brands to elevate the customer experience. For example, Netflix uses AI to recommend shows based on accumulated user’s tastes and preferences. Personalised market research optimises a firm’s resources and strengthens their customers’ engagement with, and loyalty to, the brand.
For more insights on how AI is affecting market research, check out TGM Research’s article.
How does AI Improve Brand Strategy and Decision Making?
A. Make More Informed Decisions
The effect of AI on brand strategy is akin to the impact radio and radar had on military tactics a hundred years prior. Accurate, real-time information is the most important ingredient in smart decision making. Corporate leaders can leverage AI’s automated data analysis and predictive analytics to reveal important correlations in real-time, transforming a trove of trivial information into deep insights on market trends. In doing so, companies can develop long-term, data-driven brand strategies that position themselves uniquely within their markets.
Consider mergers and acquisitions. This report from the Harvard Business Review found the failure rate of mergers and acquisitions to be around 70-90%! AI’s powerful data analysis capabilities can boost success rates by efficiently identifying risks, synergies and acquisition targets that most align with each firm’s strategic goals. It streamlines the due diligence process, leading to smarter choices.
B. Mitigate Risk
In terms of risk mitigation, AI can identify vulnerabilities in a company’s operations or branding strategies with predictive analytics and automated data management.
Real-time sentiment analysis informs branding decisions. By understanding the choices their customers are making, firms can take preventative action and pivot the company in the right direction. For example, social media posts and customer reviews can be compiled to determine whether a firm’s current campaigns are working or if they should consider investing in a rebrand to better align with their values.
C. Optimise Operations
Automated data collection, management, and analysis paired with the use of synthetic data can free up human capital traditionally used for overseeing research. Furthermore, AI can optimise operational tasks such as inventory management, logistics, and customer support to lower costs and increase efficiency. With these advantages, employees will have more opportunities to explore innovative brand strategies.
D. Capitalise on New Business Models
Complete understanding of real-time consumer preferences coupled with predictive models allows companies to develop proactive brand campaigns and product strategies. They can also use predictive analytics and synthetic data to anticipate market trends, informing the development of brand messaging that can tap into the cultural and industry zeitgeist.
Read more about AI’s impact on brand strategy and decision making from Mckinsey.
AI is a Tool, Not a Replacement (Don’t Worry, Humans are Still Required!)
Shortcomings of AI
Despite its advantages, AI has 3 major blind spots: data quality assurance, information bias, and ethics and legality.
First, AI programmes are highly dependent on the data they are trained with. Poor information and inaccurate data inputs will result in flawed outputs, and ultimately, unreliable conclusions.
Second, AI algorithms, like any code, can inherit bias from their programmers or from the data sets they were trained from. Any such biases or limitations in contextualisation could lead to discriminatory outcomes against minority populations or an overvaluation of certain groups. Any potential for discriminatory practices could cause irreparable damage to a firm’s brand image.
Finally, training AI programmes raises important legal and ethical considerations. Their heavy reliance on large swathes of data indiscriminately scrubbed from the internet has the potential to violate numerous data privacy, ownership, and security laws. Already, writers and artists are banding together to file lawsuits against AI companies to protect their work from unfair use.
To combat these risks, companies must be vigilant in their practices. AI must be trained on diverse data sets and given comprehensive inputs with relevant contextual frameworks. And most importantly, the work must be overseen by a trained human. Firms must also be prepared to collect data in ways that respect the law and store the information responsibly. This means they must ask people for their consent and adhere to data privacy laws with the utmost sincerity.
Learn more about the challenges with AI from Forbes.
Humans are Irreplaceable
The future of market research and brand strategy lies not in complete automation. At least, not yet. Blindly accepting an AI’s conclusions can be dangerous, leading to superficial insights at best, and harmful biases at worst. While AI excels at analysing information to create predictive models and synthetic data, they still lack the deep contextual understanding, flexibility, and critical thinking abilities of a human mind. Indeed, AI cannot empathise with our stakeholders. And it cannot match our creativity for pioneering visionary solutions.
Instead, the future of branding rests in the harmonious integration of artificial and human intelligence. Human analysts and engineers are still vital to the research and decision-making process. They must verify and validate results to ensure the data is accurate, complete, and unbiased. AI is nothing more than an incredible tool to augment human capabilities. But when used correctly, it can help extract truly meaningful conclusions from previously inaccessible information, paving the way for humans to develop innovative, well-informed brand strategies. Only by combining AI efficiency with human decision-making can we realise AI’s full potential.
To return to our earlier analogy, radar and radio can tell us where our problems are, but they lack the complexity and empathy to formulate a plan and execute a solution.
Photos taken from Unsplash, Freepik and Adobe Stock.
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